
AI Driven Real Time Fraud Detection and Prevention Workflow
AI-driven workflow for real-time fraud detection includes data collection preprocessing feature engineering model development and continuous monitoring for compliance
Category: AI Finance Tools
Industry: Financial Technology (FinTech)
Real-Time Fraud Detection and Prevention
1. Data Collection
1.1. Sources of Data
- Transaction records
- User behavior data
- Geolocation data
- Device information
1.2. Tools for Data Collection
- API integrations with banking systems
- Web scraping tools for market data
- Data lakes for large-scale data storage
2. Data Preprocessing
2.1. Data Cleaning
- Remove duplicates
- Handle missing values
- Standardize formats
2.2. Data Transformation
- Normalization of transaction amounts
- Encoding categorical variables
3. Feature Engineering
3.1. Identifying Key Features
- Transaction frequency
- Average transaction amount
- Time of transaction
3.2. Tools for Feature Engineering
- Python libraries (e.g., Pandas, Scikit-learn)
- Feature extraction algorithms
4. Model Development
4.1. Selecting Machine Learning Algorithms
- Random Forest
- Gradient Boosting Machines
- Neural Networks
4.2. AI-Driven Products
- IBM Watson for Fraud Detection
- Google Cloud AutoML for custom model development
5. Model Training
5.1. Training the Model
- Split data into training and test sets
- Use cross-validation techniques
5.2. Tools for Model Training
- TensorFlow
- Keras
6. Model Evaluation
6.1. Performance Metrics
- Accuracy
- Precision and Recall
- F1 Score
6.2. Tools for Evaluation
- Scikit-learn for metric calculations
- Tableau for visualization of results
7. Real-Time Deployment
7.1. Integration into Existing Systems
- API deployment for real-time scoring
- Integration with transaction processing systems
7.2. Tools for Deployment
- AWS SageMaker for model hosting
- Docker for containerization
8. Monitoring and Feedback
8.1. Continuous Monitoring
- Real-time alert systems for suspicious activities
- Feedback loop for model retraining
8.2. Tools for Monitoring
- Splunk for log analysis
- Grafana for real-time dashboards
9. Compliance and Reporting
9.1. Regulatory Compliance
- Ensure adherence to GDPR and PCI DSS
- Regular audits and compliance checks
9.2. Reporting Tools
- Power BI for reporting and analytics
- Custom dashboards for stakeholder insights
Keyword: real time fraud detection system